The present invention relates to scaling operations in a video processing system, to resize image content with minimal artifacts.
“Scaling” generally refers to processes that alter the size of an image. Scaling may be performed as a format conversion operation in a video system. For example, when video images of low resolution (e.g., 480 p) are displayed on a high resolution panel (e.g., a full high definition (1080 p) panel), the video images need to be scaled to 1080 p. This kind of conversion has been referred to as an up-conversion. Also, video images of high resolution may need to be converted to be displayed on a low resolution panel (e.g., 1080 p to 480 p) or a smaller display area (e.g., Picture in Picture (PIP)). This kind of conversion has been referred to as a down-conversion or decimation.
Typically, scaling involves adjusting size of video images to be displayed. For example, an 4:3 image frame with resolution of 480 p has 720×480, or 345,600 pixels and an 16:9 image frame with resolution of 1080 p has 1920×1080, or 2,073,600 pixels. Thus, additional pixels need to be added to perform an up-conversion from 480 p to 1080 p and existing pixels need to be eliminated and/or combined to perform a down-conversion from 1080 p to 480 p.
Traditionally, interpolation has been used to add additional pixels between existing pixels for an up-conversion and decimation has been used to eliminate and/or combining existing pixels. Both interpolation and decimation involve passing image data through signal filters (e.g., a low pass filter (LPF)). However, in a 2-dimentional filtering, there is no ideal LPF and one dimension filtering often deteriorates image content along the other dimension. Various techniques have been developed to perform the scaling and also reduce the undesirable artifacts. However, existing techniques involve complex hardware structures and do not reduce the undesirable artifacts effectively. For example, various undesirable artifacts, such as saw tooth phenomenon, edge blurring, ringing and moiré artifacts, can cause distortions to images and affect the image quality after either an up-conversion or a down-conversion, but the existing techniques using complex hardware structures do not reduce these undesirable artifacts effectively.
Accordingly, there is a need to for a scaling system that can resize image data with minimal artifacts but uses hardware structures of reduced complexity.